Transfer RNAs (tRNAs) and the genetic code underlying protein synthesis are universal to all domains of life (Dever and Green, 2012). Despite this universality, genomes show substantial variations in their preference for specific codons across their coding sequences. The source of this bias, though still debated, likely reflects selection for translational efficiency and accuracy (Drummond and Wilke, 2008; Plotkin and Kudla, 2011; Shah and Gilchrist, 2011). Importantly, even the genes within the same genome show high levels of variation in their codon preferences and synonymous codon usage bias. While rigorous proof remains lacking, there is substantial evidence linking these observed variations to different aspects of cellular biology. Given the link between protein synthesis rates, protein concentration, and optimized growth and function (Han et al., 2014; Li et al., 2014), it is conceivable that the components of translation machinery may affect protein expression levels in a concerted fashion. In Saccharomyces cerevisiae, the estimated translational speeds determined across all genes showed a significant correlation between codon usage bias and tRNA abundances, highlighting codon usage as an optimizing factor in overall cellular efficiency (Qian et al., 2012). At the same time, the role of tRNAs as direct modulator of translation efficiency in yeast has been challenged (Pop et al., 2014).
On the other hand, microarray-based analysis of tRNA abundances in various tissues has shown a significant correlation between tRNA content and codon usage bias of highly expressed tissue-specific proteins (Dittmar et al., 2006). Given that protein synthesis rate is correlated with tRNA abundance in transgene overexpression experiments (Zouridis and Hatzimanikatis, 2008), it is further hypothesized that tRNA content may effectively regulate the rate of translation for a subset of endogenous proteins (Gustafsson et al., 2004).
The nature of tRNAs makes them difficult to quantify. They have extensive secondary and tertiary structure and numerous post-transcriptional modifications that interfere with reverse transcription and hybridization. There have been previous attempts to quantify tRNA (see e.g., Dittmar et al, 2006; Zheng et al. 2015). However, such methods have various limitations, including inaccurate results due to cross-hybridization, low efficiency of obtaining full-length cDNA products, and the inability to quantify tRNAs among different species. Thus, there is a need for improved methods of tRNA quantification.